School of Geographic Sciences, East China Normal University, Shanghai 200241, China.
Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China.
Int J Environ Res Public Health. 2019 Jul 3;16(13):2352. doi: 10.3390/ijerph16132352.
Given the critical roles of nitrates and sulfates in fine particulate matter (PM) formation, we examined spatiotemporal associations between PM and sulfur dioxide (SO) as well as nitrogen dioxide (NO) in China by taking advantage of the in situ observations of these three pollutants measured from the China national air quality monitoring network for the period from 2015 to 2018. Maximum covariance analysis (MCA) was applied to explore their possible coupled modes in space and time. The relative contribution of SO and NO to PM was then quantified via a statistical modeling scheme. The linear trends derived from the stratified data show that both PM and SO decreased significantly in northern China in terms of large values, indicating a fast reduction of high PM and SO loadings therein. The statistically significant coupled MCA mode between PM and SO indicated a possible spatiotemporal linkage between them in northern China, especially over the Beijing-Tianjin-Hebei region. Further statistical modeling practices revealed that the observed PM variations in northern China could be explained largely by SO rather than NO therein, given the estimated relatively high importance of SO. In general, the evidence-based results in this study indicate a strong linkage between PM and SO in northern China in the past few years, which may help to better investigate the mechanisms behind severe haze pollution events in northern China.
鉴于硝酸盐和硫酸盐在细颗粒物(PM)形成中的关键作用,我们利用中国国家空气质量监测网络在 2015 年至 2018 年期间对这三种污染物的现场观测数据,研究了中国 PM 与二氧化硫(SO)和二氧化氮(NO)之间的时空关联。最大协方差分析(MCA)被应用于探索它们在空间和时间上可能存在的耦合模式。然后,通过统计建模方案来量化 SO 和 NO 对 PM 的相对贡献。从分层数据得出的线性趋势表明,中国北方地区 PM 和 SO 的数值都显著下降,表明该地区高 PM 和 SO 负荷的快速减少。PM 和 SO 之间具有统计学意义的 MCA 耦合模式表明,在中国北方地区,特别是在京津冀地区,它们之间可能存在时空联系。进一步的统计建模实践表明,考虑到 SO 的相对重要性,中国北方地区观测到的 PM 变化可以主要归因于 SO,而不是 NO。总的来说,本研究的基于证据的结果表明,过去几年中国北方地区 PM 和 SO 之间存在很强的联系,这可能有助于更好地研究中国北方地区严重雾霾污染事件背后的机制。